class_docstr: This layer performs an elementwise sum of its input layers.
  It requires all input layers to have the same output shape.
  
  Parameters
  ----------
  incomings : a list of :class:`Layer` instances or tuples
      the layers feeding into this layer, or expected input shapes,
      with all incoming shapes being equal
  
  coeffs: list or scalar
      A same-sized list of coefficients, or a single coefficient that
      is to be applied to all instances. By default, these will not
      be included in the learnable parameters of this layer.
  
  cropping : None or [crop]
      Cropping for each input axis. Cropping is described in the docstring
      for :func:`autocrop`
  
  Notes
  -----
  Depending on your architecture, this can be used to avoid the more
  costly :class:`ConcatLayer`. For example, instead of concatenating layers
  before a :class:`DenseLayer`, insert separate :class:`DenseLayer` instances
  of the same number of output units and add them up afterwards. (This avoids
  the copy operations in concatenation, but splits up the dot product.)
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code: def get_output_for(self, inputs, **kwargs):
      # if needed multiply each input by its coefficient
      inputs = [
          input * coeff if coeff != 1 else input
          for coeff, input in zip(self.coeffs, inputs)
      ]
  
      # pass scaled inputs to the super class for summing
      return super(ElemwiseSumLayer, self).get_output_for(
          inputs, **kwargs
      )
  
file_path: Lasagne/lasagne/layers/merge.py
imports: import theano.tensor
  from .base import MergeLayer
label_desc: Use image node_id 2 for calling the ElemwiseSumLayer obj's underlying member method code with example usage: obj.get_output_for(inputs) and returns: super